aiqbench analysis
PadLoc introduces a training-free localization method that replaces dense pose grids with sparse geometric matching to improve accuracy on floor plans without increasing state complexity. By leveraging dominant wall orientations and a Gaussian-mixture Bayes filter, the approach achieves superior performance in static sequences and robust online results for camera-only rigs in construction environments. This work advances robotics navigation by decoupling depth perception from heavy back-end optimization while maintaining modularity with existing sensor front-ends.
PadLoc: Training-Free Floor-Plan Localization by Sparse Geometric Matching
arXiv:2611.05172v1 Announce Type: cross Abstract: We propose PadLoc, a training-free localization back-end that places a moving sensor within a known floor plan. Floor plans are cheap, persistent, and robust to appearance change, but recent learned-depth methods localize against them by filtering per-frame depth through dense $(x,y,\theta)$ pose grids. PadLoc keeps the swappable depth front-end and replaces the dense grid back-end with sparse geometric matching: dominant wall orientations define a small pose-candidate set, masked normalized cross-correlation scores observed free space against the plan, and a moving-pose Gaussian-mixture Bayes filter plus consensus readout decodes the trajectory. Holding each competitor's depth fixed, PadLoc matches or beats UnLoc's forward filter and the F3Loc filter at every Gibson trajectory length, and UnLoc's offline refinement from $T=35$ on; it exceeds Z-FLoc's published results with a shared MapAnything front-end. On Bonn LiLocBench, PadLoc improves pooled SR@1m over UnLoc by 23-32 pp under wheel odometry and 35-43 pp with ground-truth motion; with full-FOV LiDAR plus wheel odometry the same readout reaches 100% SR@1m at 9-10 cm median error on the static sequence. On the Hilti-Trimble Localization Challenge PadLoc localizes a camera-only fisheye rig on construction floors hundreds of metres across at 0.90-0.98 online SR@2m on three of five sessions; adding learned wall isolation, applied to every session, covers all five at 0.67-0.92 with the localizer unchanged throughout. PadLoc thus matches or outperforms dense grid back-ends while preserving sensor modularity and a compact state that does not grow with trajectory length.
Abstract:We propose PadLoc, a training-free localization back-end that places a moving sensor within a known floor plan. Floor plans are cheap, persistent, and robust to appearance change, but recent learned-depth methods localize against them by filtering per-frame depth through dense $(x,y,\theta)$ pose grids. PadLoc keeps the swappable depth front-end and replaces the dense grid back-end with sparse geometric matching: dominant wall orientations define a small pose-candidate set, masked normalized cross-correlation scores observed free space against the plan, and a moving-pose Gaussian-mixture Bayes filter plus consensus readout decodes the trajectory. Holding each competitor's depth fixed, PadLoc matches or beats UnLoc's forward filter and the F3Loc filter at every Gibson trajectory length, and UnLoc's offline refinement from $T=35$ on; it exceeds Z-FLoc's published results with a shared MapAnything front-end. On Bonn LiLocBench, PadLoc improves pooled SR@1m over UnLoc by 23-32 pp under wheel odometry and 35-43 pp with ground-truth motion; with full-FOV LiDAR plus wheel odometry the same readout reaches 100% SR@1m at 9-10 cm median error on the static sequence. On the Hilti-Trimble Localization Challenge PadLoc localizes a camera-only fisheye rig on construction floors hundreds of metres across at 0.90-0.98 online SR@2m on three of five sessions; adding learned wall isolation, applied to every session, covers all five at 0.67-0.92 with the localizer unchanged throughout. PadLoc thus matches or outperforms dense grid back-ends while preserving sensor modularity and a compact state that does not grow with trajectory length.
Accepted to WACV 2027 (Algorithms track). 22 pages: main paper (10 pages incl. references) and supplementary material (12 pages). Code: this https URL
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
arXiv:2611.05172 [cs.CV]
(or arXiv:2611.05172v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.05172 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 283 words